Relevant bibliographies by topics / Discrete optimal control problems

Academic literature on the topic 'Discrete optimal control problems'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Discrete optimal control problems"

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Ulybyshev, Yuri. "Discrete Pseudocontrol Sets for Optimal Control Problems." Journal of Guidance, Control, and Dynamics 33, no. 4 (July 2010): 1133–42. http://dx.doi.org/10.2514/1.47315.

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Marinković, Boban. "Sensitivity analysis for discrete optimal control problems." Mathematical Methods of Operations Research 63, no. 3 (November 10, 2005): 513–24. http://dx.doi.org/10.1007/s00186-005-0029-1.

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Marinković, Boban. "Optimality conditions for discrete optimal control problems." Optimization Methods and Software 22, no. 6 (December 2007): 959–69. http://dx.doi.org/10.1080/10556780701485314.

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Chryssoverghi, I., and A. Bacopoulos. "Discrete approximation of relaxed optimal control problems." Journal of Optimization Theory and Applications 65, no. 3 (June 1990): 395–407. http://dx.doi.org/10.1007/bf00939558.

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Teo, K. L., Y. Liu, and C. J. Goh. "Nonlinearly constrained discrete-time optimal-control problems." Applied Mathematics and Computation 38, no. 3 (August 1990): 227–48. http://dx.doi.org/10.1016/0096-3003(90)90024-w.

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Zhang, Ying, Changjun Yu, Yingtao Xu, and Kok Lay Teo. "Minimizing control variation in discrete-time optimal control problems." Journal of Computational and Applied Mathematics 292 (January 2016): 292–306. http://dx.doi.org/10.1016/j.cam.2015.07.010.

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Ding, Wandi, Raymond Hendon, Brandon Cathey, Evan Lancaster, and Robert Germick. "Discrete time optimal control applied to pest control problems." Involve, a Journal of Mathematics 7, no. 4 (May 31, 2014): 479–89. http://dx.doi.org/10.2140/involve.2014.7.479.

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Lefebvre, Mario, and Moussa Kounta. "Discrete homing problems." Archives of Control Sciences 23, no. 1 (March 1, 2013): 5–18. http://dx.doi.org/10.2478/v10170-011-0039-6.

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Abstract We consider the so-called homing problem for discrete-time Markov chains. The aim is to optimally control the Markov chain until it hits a given boundary. Depending on a parameter in the cost function, the optimizer either wants to maximize or minimize the time spent by the controlled process in the continuation region. Particular problems are considered and solved explicitly. Both the optimal control and the value function are obtained
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Philipp, Eduardo A., Laura S. Aragone, and Lisandro A. Parente. "Discrete time schemes for optimal control problems with monotone controls." Computational and Applied Mathematics 34, no. 3 (May 28, 2014): 847–63. http://dx.doi.org/10.1007/s40314-014-0149-4.

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Apanapudor, J. S., F. M. Aderibigbe, and F. Z. Okwonu. "An Optimal Penalty Constant For Discrete Optimal Control Regulator Problems." Journal of Physics: Conference Series 1529 (April 2020): 042073. http://dx.doi.org/10.1088/1742-6596/1529/4/042073.

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Dissertations / Theses on the topic "Discrete optimal control problems"

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Woon, Siew Fang. "Global algorithms for nonlinear discrete optimization and discrete-valued optimal control problems." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/538.

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Optimal control problems arise in many applications, such as in economics, finance, process engineering, and robotics. Some optimal control problems involve a control which takes values from a discrete set. These problems are known as discrete-valued optimal control problems. Most practical discrete-valued optimal control problems have multiple local minima and thus require global optimization methods to generate practically useful solutions. Due to the high complexity of these problems, metaheuristic based global optimization techniques are usually required.One of the more recent global optimization tools in the area of discrete optimization is known as the discrete filled function method. The basic idea of the discrete filled function method is as follows. We choose an initial point and then perform a local search to find an initial local minimizer. Then, we construct an auxiliary function, called a discrete filled function, at this local minimizer. By minimizing the filled function, either an improved local minimizer is found or one of the vertices of the constraint set is reached. Otherwise, the parameters of the filled function are adjusted. This process is repeated until no better local minimizer of the corresponding filled function is found. The final local minimizer is then taken as an approximation of the global minimizer.While the main aim of this thesis is to present a new computational methodfor solving discrete-valued optimal control problems, the initial focus is on solvingpurely discrete optimization problems. We identify several discrete filled functionstechniques in the literature and perform a critical review including comprehensive numerical tests. Once the best filled function method is identified, we propose and test several variations of the method with numerical examples.We then consider the task of determining near globally optimal solutions of discrete-valued optimal control problems. The main difficulty in solving the discrete-valued optimal control problems is that the control restraint set is discrete and hence not convex. Conventional computational optimal control techniques are designed for problems in which the control takes values in a connected set, such as an interval, and thus they cannot solve the problem directly. Furthermore, variable switching times are known to cause problems in the implementation of any numerical algorithm due to the variable location of discontinuities in the dynamics. Therefore, such problem cannot be solved using conventional computational approaches. We propose a time scaling transformation to overcome this difficulty, where a new discrete variable representing the switching sequence and a new variable controlling the switching times are introduced. The transformation results in an equivalent mixed discrete optimization problem. The transformed problemis then decomposed into a bi-level optimization problem, which is solved using a combination of an efficient discrete filled function method identified earlier and a computational optimal control technique based on the concept of control parameterization.To demonstrate the applicability of the proposed method, we solve two complex applied engineering problems involving a hybrid power system and a sensor scheduling task, respectively. Computational results indicate that this method is robust, reliable, and efficient. It can successfully identify a near-global solution for these complex applied optimization problems, despite the demonstrated presence of multiple local optima. In addition, we also compare the results obtained with other methods in the literature. Numerical results confirm that the proposed method yields significant improvements over those obtained by other methods.
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Rieck, Rainer Matthias [Verfasser], Florian [Akademischer Betreuer] [Gutachter] Holzapfel, and Matthias [Gutachter] Gerdts. "Discrete Controls and Constraints in Optimal Control Problems / Rainer Matthias Rieck ; Gutachter: Matthias Gerdts, Florian Holzapfel ; Betreuer: Florian Holzapfel." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1126644137/34.

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Ferraço, Igor Breda. "Controle ótimo por modos deslizantes via função penalidade." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-09112011-161224/.

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Este trabalho aborda o problema de controle ótimo por modos deslizantes via função penalidade para sistemas de tempo discreto. Para resolver este problema será desenvolvido uma estrutura matricial alternativa baseada no problema de mínimos quadrados ponderados e funções penalidade. A partir desta nova formulação é possível obter a lei de controle ótimo por modos deslizantes, as equações de Riccati e a matriz do ganho de realimentação através desta estrutura matricial alternativa. A motivação para propormos essa nova abordagem é mostrar que é possível obter uma solução alternativa para o problema clássico de controle ótimo por modos deslizantes.
This work introduces a penalty function approach to deal with the optimal sliding mode control problem for discrete-time systems. To solve this problem an alternative array structure based on the problem of weighted least squares penalty function will be developed. Using this alternative matrix structure, the optimal sliding mode control law of, the matrix Riccati equations and feedback gain were obtained. The motivation of this new approach is to show that it is possible to obtain an alternative solution to the classic problem of optimal sliding mode control.
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Hazell, Andrew. "Discrete-time optimal preview control." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/8472.

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There are many situations in which one can preview future reference signals, or future disturbances. Optimal Preview Control is concerned with designing controllers which use this preview to improve closed-loop performance. In this thesis a general preview control problem is presented which includes previewable disturbances, dynamic weighting functions, output feedback and nonpreviewable disturbances. It is then shown how a variety of problems may be cast as special cases of this general problem; of particular interest is the robust preview tracking problem and the problem of disturbance rejection with uncertainty in the previewed signal. The general preview problem is solved in both the Fh and Beo settings. The H2 solution is a relatively straightforward extension ofpreviously known results, however, our contribution is to provide a single framework that may be used as a reference work when tackling a variety of preview problems. We also provide some new analysis concerning the maximum possible reduction in closed-loop H2 norm which accrues from the addition of preview action. The solution to the Hoo problem involves a completely new approach to Hoo preview control, in which the structure of the associated Riccati equation is exploited in order to find an efficient algorithm for computing the optimal controller. The problem tackled here is also more generic than those previously appearing in the literature. The above theory finds obvious applications in the design of controllers for autonomous vehicles, however, a particular class of nonlinearities found in typical vehicle models presents additional problems. The final chapters are concerned with a generic framework for implementing vehicle preview controllers, and also a'case study on preview control of a bicycle.
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Soler, Edilaine Martins. "Resolução do problema de fluxo de potência ótimo com variáveis de controle discretas." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-07042011-151716/.

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O objetivo de um problema de Fluxo de Potência Ótimo é determinar o estado de um sistema de transmissão de energia elétrica que otimize um dado desempenho do sistema, e satisfaça suas restrições físicas e operacionais. O problema de Fluxo de Potência Ótimo é modelado como um problema de programação não linear com variáveis discretas e contínuas. Na maioria das abordagens da literatura para a resolução de problemas de Fluxo de Potência Ótimo, os controles discretos são modelados como variáveis contínuas. Estas formulações estão longe da realidade de um sistema elétrico pois alguns controles podem somente ser ajustados por passos discretos. Este trabalho apresenta um método para tratar as variáveis discretas do problema de Fluxo de Potência Ótimo. Uma função, que penaliza a função objetivo quando as variáveis discretas assumem valores não discretos, é apresentada. Ao incorporar esta função na função objetivo, um problema de programação não linear com somente variáveis contínuas é obtido e a solução desse problema é equivalente à solução do problema original, que contém variáveis discretas e contínuas. O problema de programação não linear é resolvido pelo Método de Pontos Interiores com Filtro. Experimentos numéricos com os sistemas elétricos IEEE 14, 30, 118 e 300 Barras comprovam que a abordagem proposta é eficiente na resolução de problemas de Fluxo de Potência Ótimo.
The aim of solving the Optimal Power Flow problem is to determine the state of an electric power transmission system that optimizes a given system performance, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. In most techniques existing in the literature to solve the Optimal Power Flow problems, the discrete controls are modeled as continuous variables. These formulations are unrealistic, as some controls can be set only to values taken from a given set of discrete values. This study proposes a method for handling the discrete variables of the Optimal Power Flow problem. A function, which penalizes the objective function when discrete variables assume non-discrete values, is presented. By including this penalty function into the objective function, a nonlinear programming problem with only continuous variables is obtained and the solution of this problem is equivalent to the solution of the initial problem that contains discrete and continuous variables. The nonlinear programming problem is solved by a Interior-Point Method with filter line-search. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the proposed approach is efficient in the resolution of OPF problems.
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Huang, Hongqing. "Algorithms for optimal feedback control problems." Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1177101576.

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Seywald, Hans. "Optimal control problems with switching points." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-07282008-135220/.

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Barth, Eric J. "Approximating discrete-time optimal control using a neural network." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/19009.

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Cheung, Ka-chun. "Optimal asset allocation problems under the discrete-time regime-switching model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31311234.

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Cheung, Ka-chun, and 張家俊. "Optimal asset allocation problems under the discrete-time regime-switching model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31311234.

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Books on the topic "Discrete optimal control problems"

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Ralph, Daniel. A parallel method for discrete-time optimal control problems. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1993.

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Liao, Aiping. Solving unconstrained discrete-time optimal control problems using trust region method. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1995.

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Liao, Aiping. Some efficient algorithms for unconstrained discrete-time optimal control problems. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1993.

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Zaslavski, Alexander J. Stability of the Turnpike Phenomenon in Discrete-Time Optimal Control Problems. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08034-5.

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Coleman, Thomas F. An efficient trust region method for unconstrained discrete-time optimal control problems. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1993.

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Liao, Li-zhi. Advantages of differential dynamic programming over Newton's method for discrete-time optimal control problems. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1992.

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Chen, Tongwen. Optimal sampled-data control systems. London: Springer, 1995.

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Bertsekas, Dimitri P. Stochastic optimal control: The discrete time case. Belmont, Mass: Athena Scientific, 1996.

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Constrained control problems of discrete processes. Singapore: World Scientific, 1996.

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Nonconvex optimal control and variational problems. New York, NY: Springer, 2013.

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Book chapters on the topic "Discrete optimal control problems"

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Ma, Zhongjing, and Suli Zou. "Discrete-Time Optimal Control Problems." In Optimal Control Theory, 277–341. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6292-5_7.

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Teo, Kok Lay, Bin Li, Changjun Yu, and Volker Rehbock. "Discrete Time Optimal Control Problems." In Applied and Computational Optimal Control, 121–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69913-0_5.

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Zaslavski, Alexander J. "Discrete-Time Autonomous Problems." In Turnpike Conditions in Infinite Dimensional Optimal Control, 25–130. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20178-4_2.

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Seierstad, Atle. "Piecewise Deterministic Optimal Control Problems." In Stochastic Control in Discrete and Continuous Time, 1–70. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76617-1_3.

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Chryssoverghi, Ion. "Discrete Methods for Optimal Control Problems." In Numerical Methods and Applications, 205–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36487-0_22.

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Zaslavski, Alexander J. "Optimal Control Problems with Discounting." In Stability of the Turnpike Phenomenon in Discrete-Time Optimal Control Problems, 47–63. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08034-5_3.

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Arnold, E., and H. Puta. "An SQP-type Solution Method for Constrained Discrete-Time Optimal Control Problems." In Computational Optimal Control, 127–36. Basel: Birkhäuser Basel, 1994. http://dx.doi.org/10.1007/978-3-0348-8497-6_11.

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Zaslavski, Alexander J. "Discrete-Time Nonautonomous Problems on Axis." In Turnpike Conditions in Infinite Dimensional Optimal Control, 197–267. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20178-4_4.

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Zaslavski, Alexander J. "Turnpike Properties of Discrete-Time Problems." In Turnpike Phenomenon and Infinite Horizon Optimal Control, 23–145. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08828-0_2.

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Leitmann, George, and Cho Seng Lee. "A Discrete Stabilizing Study Strategy for a Student Related Problem under Uncertainty." In Optimal Control, 173–85. Basel: Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-7539-4_13.

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Conference papers on the topic "Discrete optimal control problems"

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Kong, Fang-Di. "Optimality Conditions for Discrete Optimal Control Problems." In 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icwcsn-16.2017.115.

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DURAZZI, CARLA, and EMANUELE GALLIGANI. "NUMERICAL SOLUTION OF DISCRETE QUADRATIC OPTIMAL CONTROL PROBLEMS." In Proceedings of the Fourth International Conference. WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814291071_0072.

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Rieck, Matthias, Maximilian Richter, and Florian Holzapfel. "Discrete Control Dependent Constraints in Multiple Shooting Optimal Control Problems." In AIAA Guidance, Navigation, and Control (GNC) Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-4526.

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Guibout, V., and A. Bloch. "A discrete maximum principle for solving optimal control problems." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1430309.

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"EVOLUTIONARY COMPUTATION FOR DISCRETE AND CONTINUOUS TIME OPTIMAL CONTROL PROBLEMS." In 2nd International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001171200450054.

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Hussein, Islam I., Melvin Leok, Amit K. Sanyal, and Anthony M. Bloch. "A Discrete Variational Integrator for Optimal Control Problems on SO(3)." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377818.

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Dykhta, Vladimir Aleksandrovich, Ol'ga Nikolaevna Samsonyuk, and Stepan Pavlovich Sorokin. "Feedback minimum principle for continuous, discrete and impulsive optimal control problems." In International Conference "Optimal Control and Differential Games" dedicated to the 110th anniversary of L. S. Pontryagin. Moscow: Steklov Mathematical Institute, 2018. http://dx.doi.org/10.4213/proc22973.

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Chen, Dijian, Kenji Fujimoto, and Tatsuya Suzuki. "Double generating function approach to discrete-time nonlinear optimal control problems." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402825.

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Meherrem, Sahlar, and Galina Kurina. "Decomposition of discrete linear-quadratic optimal control problems for switching systems." In The 10th AIMS Conference on Dynamical Systems, Differential Equations and Applications (Madrid, Spain). American Institute of Mathematical Sciences, 2015. http://dx.doi.org/10.3934/proc.2015.0764.

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Ulybyshev, Yuri. "Discrete Pseudo-Control Sets for Optimal Control Problem." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5788.

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Reports on the topic "Discrete optimal control problems"

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Cao, Yanzhao. Numerical Solutions for Optimal Control Problems Under SPDE Constraints. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada458787.

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Cao, Yanzhao. Numerical Solutions for Optimal Control Problems Under SPDE Constraints. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada480192.

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Iyer, R. V., R. Holsapple, and D. Doman. Optimal Control Problems on Parallelizable Riemannian Manifolds: Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada455175.

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Meyer, Gerard G., and Howard L. Weinert. Fault Tolerant Parallel Implementations of Iterative Algorithms for Optimal Control Problems. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada214786.

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Meyer, Gerard G., and Howard L. Weinert. Fault Tolerant Parallel Implementations of Iterative Algorithms for Optimal Control Problems. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada198041.

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Kushner, Harold J. Approximations and Optimal Control for the Pathwise Average Cost per Unit Time and Discounted Problems for Wideband Noise Driven Systems,. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada192712.

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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by computing optimal energy paths around the Channel Islands in the Santa Barbara bay using time-varying flow field forecasts generated by the Regional Ocean Model System. We verify the accuracy of the computed paths by comparing them with paths computed via an optimal control formulation.
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Li, Yan, Yuhao Luo, and Xin Lu. PHEV Energy Management Optimization Based on Multi-Island Genetic Algorithm. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0739.

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The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor. Afterwards, based on the Isight and ADVISOR co-simulation platform, with the goal of ensuring certain dynamic performance and optimal fuel economy of the vehicle, the multi-island genetic algorithm is used to optimize the parameters of the membership function of the fuzzy control strategy to overcome it to a certain extent. The disadvantages of selecting parameters based on experience are compensated for, and the efficiency and feasibility of fuzzy control are improved. Finally, the PHEV vehicle model simulation comparison was carried out under the UDDS working condition through ADVISOR software. The optimization results show that while ensuring the required power performance, the vehicle fuzzy controller after parameter optimization using the multi-island genetic algorithm is more efficient, which can significantly reduce vehicle fuel consumption and improve exhaust emissions.
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Chamovitz, Daniel A., and Xing-Wang Deng. Developmental Regulation and Light Signal Transduction in Plants: The Fus5 Subunit of the Cop9 Signalosome. United States Department of Agriculture, September 2003. http://dx.doi.org/10.32747/2003.7586531.bard.

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Plants adjust their growth and development in a manner optimal for the prevailing light conditions. The molecular mechanisms by which light signals are transduced and integrated with other environmental and developmental signals are an area of intense research. (Batschauer, 1999; Quail, 2002) One paradigm emerging from this work is the interconnectedness of discrete physiological responses at the biochemical level, for instance, between auxin and light signaling (Colon-Carmona et al., 2000; Schwechheimer and Deng, 2001; Tian and Reed, 1999) and between light signaling and plant pathogen interactions (Azevedo et al., 2002; Liu et al., 2002). The COP9 signalosome (CSN) protein complex has a central role in the light control of plant development. Arabidopsis mutants that lack this complex develop photomorphogenically even in the absence of light signals (reviewed in (Karniol and Chamovitz, 2000; Schwechheimer and Deng, 2001). Thus the CSN was hypothesized to be a master repressor of photomorphogenesis in darkness, and light acts to bypass or eliminate this repression. However, the CSN regulates more than just photomorphogenesis as all mutants lacking this complex die near the end of seedling development. Moreover, an essentially identical complex was subsequently discovered in animals and yeast, organisms whose development is not light responsive, exemplifying how plant science can lead the way to exciting discoveries in biomedical model species (Chamovitz and Deng, 1995; Freilich et al., 1999; Maytal-Kivity et al., 2002; Mundt et al., 1999; Seeger et al., 1998; Wei et al., 1998). Our long-term objective is to determine mechanistically how the CSN controls plant development. We previously that this complex contains eight subunits (Karniol et al., 1998; Serino et al., 1999) and that the 27 ilia subunit is encoded by the FUS5/CSN7 locus (Karniol et al., 1999). The CSN7 subunit also has a role extraneous to the COP9 signalosome, and differential kinase activity has been implicated in regulating CSN7 and the COP9 signalosome (Karniol et al., 1999). In the present research, we further analyzed CSN7, both in terms of interacting proteins and in terms of kinases that act on CSN7. Furthermore we completed our analysis of the CSN in Arabidopsis by analyzing the remaining subunits. Outline of Original Objectives and Subsequent Modifications The general goal of the proposed research was to study the CSN7 (FUS5) subunit of the COP9 signalosome. To this end we specifically intended to: 1. Identify the residues of CSN7 that are phosphorylated. 2. Monitor the phosphorylation of CSN7 under different environmental conditions and under different genetic backgrounds. 3. Generate transgenic plants with altered CSN7 phosphorylation sites. 4. Purify CSN7 kinase from cauliflower. 5. Clone the Arabidopsis cDNA encoding CSN7 kinase 6. Isolate and characterize additional CSN7 interacting proteins. 7. Characterize the interaction of CSN7 and the COP9 signalosome with the HY5-COP1 transcriptional complex. Throughout the course of the research, emphasis shifted from studying CSN7 phosphorylation (Goals 1-3), to studying the CSN7 kinase (Goal 4 and 5), an in depth analysis of CSN7 interactions (Goal 6), and the study of additional CSN subunits. Goal 7 was also abandoned as no data was found to support this interaction.
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10

Poverenov, Elena, Tara McHugh, and Victor Rodov. Waste to Worth: Active antimicrobial and health-beneficial food coating from byproducts of mushroom industry. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600015.bard.

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Background. In this proposal we suggest developing a common solution for three seemingly unrelated acute problems: (1) improving sustainability of fast-growing mushroom industry producing worldwide millions of tons of underutilized leftovers; (2) alleviating the epidemic of vitamin D deficiency adversely affecting the public health in both countries and in other regions; (3) reducing spoilage of perishable fruit and vegetable products leading to food wastage. Based on our previous experience we propose utilizing appropriately processed mushroom byproducts as a source of two valuable bioactive materials: antimicrobial and wholesome polysaccharide chitosan and health-strengthening nutrient ergocalciferol⁽ᵛⁱᵗᵃᵐⁱⁿ ᴰ2⁾. ᴬᵈᵈⁱᵗⁱᵒⁿᵃˡ ᵇᵉⁿᵉᶠⁱᵗ ᵒᶠ ᵗʰᵉˢᵉ ᵐᵃᵗᵉʳⁱᵃˡˢ ⁱˢ ᵗʰᵉⁱʳ ᵒʳⁱᵍⁱⁿ ᶠʳᵒᵐ ⁿᵒⁿ⁻ᵃⁿⁱᵐᵃˡ ᶠᵒᵒᵈ⁻ᵍʳᵃᵈᵉ source. We proposed using chitosan and vitamin D as ingredients in active edible coatings on two model foods: highly perishable fresh-cut melon and less perishable health bars. Objectives and work program. The general aim of the project is improving storability, safety and health value of foods by developing and applying a novel active edible coating based on utilization of mushroom industry leftovers. The work plan includes the following tasks: (a) optimizing the UV-B treatment of mushroom leftover stalks to enrich them with vitamin D without compromising chitosan quality - Done; (b) developing effective extraction procedures to yield chitosan and vitamin D from the stalks - Done; (c) utilizing LbL approach to prepare fungal chitosan-based edible coatings with optimal properties - Done; (d) enrichment of the coating matrix with fungal vitamin D utilizing molecular encapsulation and nano-encapsulation approaches - Done, it was found that no encapsulation methods are needed to enrich chitosan matrix with vitamin D; (e) testing the performance of the coating for controlling spoilage of fresh cut melons - Done; (f) testing the performance of the coating for nutritional enhancement and quality preservation of heath bars - Done. Achievements. In this study numerous results were achieved. Mushroom waste, leftover stalks, was treated ʷⁱᵗʰ ᵁⱽ⁻ᴮ ˡⁱᵍʰᵗ ᵃⁿᵈ ᵗʳᵉᵃᵗᵐᵉⁿᵗ ⁱⁿᵈᵘᶜᵉˢ ᵃ ᵛᵉʳʸ ʰⁱᵍʰ ᵃᶜᶜᵘᵐᵘˡᵃᵗⁱᵒⁿ ᵒᶠ ᵛⁱᵗᵃᵐⁱⁿ ᴰ2, ᶠᵃʳ ᵉˣᶜᵉᵉᵈⁱⁿᵍ any other dietary vitamin D source. The straightforward vitamin D extraction procedure and ᵃ ˢⁱᵐᵖˡⁱᶠⁱᵉᵈ ᵃⁿᵃˡʸᵗⁱᶜᵃˡ ᵖʳᵒᵗᵒᶜᵒˡ ᶠᵒʳ ᵗⁱᵐᵉ⁻ᵉᶠᶠⁱᶜⁱᵉⁿᵗ ᵈᵉᵗᵉʳᵐⁱⁿᵃᵗⁱᵒⁿ ᵒᶠ ᵗʰᵉ ᵛⁱᵗᵃᵐⁱⁿ ᴰ2 ᶜᵒⁿᵗᵉⁿᵗ suitable for routine product quality control were developed. Concerning the fungal chitosan extraction, new freeze-thawing protocol was developed, tested on three different mushroom sources and compared to the classic protocol. The new protocol resulted in up to 2-fold increase in the obtained chitosan yield, up to 3-fold increase in its deacetylation degree, high whitening index and good antimicrobial activity. The fungal chitosan films enriched with Vitamin D were prepared and compared to the films based on animal origin chitosan demonstrating similar density, porosity and water vapor permeability. Layer-by-layer chitosan-alginate electrostatic deposition was used to coat fruit bars. The coatings helped to preserve the quality and increase the shelf-life of fruit bars, delaying degradation of ascorbic acid and antioxidant capacity loss as well as reducing bar softening. Microbiological analyses also showed a delay in yeast and fungal growth when compared with single layer coatings of fungal or animal chitosan or alginate. Edible coatings were also applied on fresh-cut melons and provided significant improvement of physiological quality (firmness, weight ˡᵒˢˢ⁾, ᵐⁱᶜʳᵒᵇⁱᵃˡ ˢᵃᶠᵉᵗʸ ⁽ᵇᵃᶜᵗᵉʳⁱᵃ, ᵐᵒˡᵈ, ʸᵉᵃˢᵗ⁾, ⁿᵒʳᵐᵃˡ ʳᵉˢᵖⁱʳᵃᵗⁱᵒⁿ ᵖʳᵒᶜᵉˢˢ ⁽Cᴼ2, ᴼ²⁾ ᵃⁿᵈ ᵈⁱᵈ not cause off-flavor (EtOH). It was also found that the performance of edible coating from fungal stalk leftovers does not concede to the chitosan coatings sourced from animal or good quality mushrooms. Implications. The proposal helped attaining triple benefit: valorization of mushroom industry byproducts; improving public health by fortification of food products with vitamin D from natural non-animal source; and reducing food wastage by using shelf- life-extending antimicrobial edible coatings. New observations with scientific impact were found. The program resulted in 5 research papers. Several effective and straightforward procedures that can be adopted by mushroom growers and food industries were developed. BARD Report - Project 4784
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